论文标题
Donex：3D点云的实时占用网格的动态回声分类
DONEX: Real-time occupancy grid based dynamic echo classification for 3D point cloud
论文作者
论文摘要
为了驾驶援助和自主驾驶系统，重要的是要区分动态物体，例如移动车辆和静态物体，例如护栏。在所有传感器模式中，雷达和FMCW激光雷达可以提供有关原始测量数据运动状态的信息。另一方面，使用TOF LIDAR测量数据的感知管道通常只能区分对象级别上的动态和静态状态。在这项工作中，开发了一种称为DONEX的新算法，以使用占用网格方法对3D激光点云回波的运动状态进行分类。通过算法改进，例如2D网格方法，可以减少运行时。还考虑了测量传感器位于移动车辆中的场景。
For driving assistance and autonomous driving systems, it is important to differentiate between dynamic objects such as moving vehicles and static objects such as guard rails. Among all the sensor modalities, RADAR and FMCW LiDAR can provide information regarding the motion state of the raw measurement data. On the other hand, perception pipelines using measurement data from ToF LiDAR typically can only differentiate between dynamic and static states on the object level. In this work, a new algorithm called DONEX was developed to classify the motion state of 3D LiDAR point cloud echoes using an occupancy grid approach. Through algorithmic improvements, e.g. 2D grid approach, it was possible to reduce the runtime. Scenarios, in which the measuring sensor is located in a moving vehicle, were also considered.
